# Dynamic Capabilities

An organisation's ability to sense change, seize opportunity and reconfigure its resources accordingly.

**Rests on:** Organizational Ambidexterity

*Business, Career and Human Factors · Talent Management*


## Why learn this

Think about the last time your company announced a new strategy, merged with another firm, or switched to a completely new software system. You likely felt a brief flurry of activity followed by a return to business as usual, but the *rules* of how you worked had changed. Most management advice tells you how to hire for today’s jobs. This concept asks how you prepare for tomorrow’s unknown needs. When the market shifts—for example, when a competitor releases a product that makes your company’s main offering obsolete—the organisation must be able to sense that shift quickly, gather the right people to respond, and reorganise its work before it loses too much ground. If your talent systems are static, they will preserve the old way of doing things long after the world has moved on. Mastering this idea means you stop asking 'Do we have enough staff?' and start asking 'Can our people adapt fast enough when the goalposts move?'


## The intuition

Imagine a company that has sold printed encyclopedias for thirty years. It owns large warehouses full of books, employs excellent salespeople who know how to visit schools and libraries, and has brand recognition that everyone trusts. These are its current strengths. Then, the internet becomes widespread, and people start downloading information instead of buying paper volumes. Sales drop by 40% in two years. The company still has the warehouses, the sales team, and the brand, but these assets no longer match what customers want. A company without this specific skill set tries to sell more books or lower their price. It fails because the market itself has changed.

Now imagine a different approach. When the first signs of digital download appear, a second company doesn't cling to its printing press. It senses the change in how people consume information. It seizes the opportunity by using its existing trust and brand to launch a subscription service for digital articles. It transforms its workforce: it re-trains some editors for screen-based content, hires data engineers to recommend articles based on reading habits, and shifts its sales team from visiting libraries to managing corporate accounts for the new software. It lost its primary product, but it kept its ability to be useful. This second company has 'dynamic capabilities'. It is not just good at making encyclopedias; it is good at changing what it makes when the environment forces it to.

Notice the distinction here. A regular capability is a fixed skill: being able to bake bread. A dynamic capability is the ability to learn how to bake sourdough, then switch to selling vegan gluten-free cakes, because the customers changed. It is a 'skill about skills'. It lives in your hiring practices, how you reward employees, and how quickly you allow teams to experiment. You cannot put it in a manual. You build it by creating an environment where change feels like an opportunity rather than a threat.


## An analogy

Consider a professional cricket team preparing for a tournament. The team has resources: a star batter with high average scores, a bowler who takes many wickets on fast pitches, and a coach with a specific playbook. These are valuable assets in one context. Now, imagine the tournament moves to a venue where the pitch is dry and dusty, favoring slow spinners who take time to build pressure. The star batter’s power hitting becomes less effective, and the fast bowler struggles to get movement. If the team relies only on their existing 'capabilities' (their fixed skills), they will lose.

A team with dynamic capabilities does three things. First, it *senses* the change early by analysing pitch conditions and opponent tactics before the first ball is bowled. Second, it *seizes* the moment by promoting a spinner who might not have played as much recently, or by adjusting the batting order to prioritize patience over power. Third, it *transforms* its routine by changing its practice drills to focus on rotation of strike rather than big hits. The players (resources) remain the same, but the way they are used and prepared changes. The analogy breaks down slightly because a cricket team knows exactly when the tournament ends and what the rules are; a company faces continuous, unpredictable change with no clear finish line. However, the core lesson holds: in a changing environment, who adapts their strategy faster wins, not who started with the strongest individual players.


## Definition

Dynamic capabilities are an organization’s consistent ability to detect changes in its market or technology, act on those changes by recombining its people and resources, and update its internal processes so that it remains successful as conditions shift.


## How it connects

This topic sits alongside 'Succession Planning'. Succession planning usually focuses on replacing a specific person in a specific role; dynamic capabilities look at whether the organization has a pool of people who can step into *new* roles created by change. It also connects with 'Adaptive Expertise'. Adaptive expertise describes an individual’s ability to apply their knowledge flexibly in novel situations, which is the building block that allows the entire organization to exhibit dynamic capabilities.


## The mathematics

Dynamic capabilities as a concept don't require any calculation — that's a first. But there *is* a hidden piece of mathematics under the surface, and it is the branch called *arithmetic*, the same arithmetic you use to compare two amounts. The claim 'advantage comes from reconfiguring faster than rivals' is a claim about *rates* — how much change you can do in a unit of time. A rate is just a division: the amount of change divided by the time taken. If your rival can reconfigure its sales team in two months, and you can do it in one, your 'reconfiguration speed' is double theirs. That's the whole mathematics you need. The reconfiguration itself involves swapping one set of numbers for another — but no symbols are needed. So the 'verbal' mathematics here is simply: take the size of the change, divide by the time it takes, and compare that number between two organisations. The art lies in which numbers you choose to measure, and in remembering that the change is not just a number but a transformation of what the organisation can do.

Let C be the amount of change (the new capability, measured as 'one complete reconfiguration' — call it 1), and t the time it takes. Reconfiguration speed = C / t. Since C is a single reconfiguration, C = 1, so speed = 1 / t.


## A worked example

```
Two rival delivery companies, Swift and Steady, both notice that customers now prefer app-based ordering. They both see a drop in phone orders. Swift already has a strong app team; Steady must build a new team from scratch. We measure their reconfiguration speed in 'weeks taken to launch a working ordering app'. Swift takes 4 weeks; Steady takes 12 weeks.

Step 1: Change = new capability launched: app ordering   # Both companies aim for the same change: a working app.
Step 2: Swift's time = 4 weeks   # Because they already had the skills and infrastructure.
Step 3: Steady's time = 12 weeks   # Because they had to hire, train, and build the team from zero.
Step 4: Swift's speed = 1 change / 4 weeks = 0.25 changes per week   # That's the rate: one change completed in four weeks.
Step 5: Steady's speed = 1 change / 12 weeks = 0.083 changes per week   # The same one change took three times as long.

Result: Swift's reconfiguration speed is exactly three times Steady's (0.25 ÷ 0.083 ≈ 3). Even though both finish, Swift gains a three-month head start in a market that rewards speed. That head start *is* the competitive advantage — not the app itself, but how quickly they could build it.
```


## From scratch, in Python

```python
# We don't code the strategy itself, but we can model how
# reconfiguration speed plays out. Here we compare two companies.
# Each step = one week. A 'reconfiguration' takes a certain number
# of weeks; we let time pass until both have finished.

# First, the building blocks we need:
# - a list to hold each company's remaining weeks to completion
# - a number for how many weeks have passed
# - a loop that repeats until no company is left

# Python building blocks: a *list* keeps things in order, e.g.
# companies = ["Swift", "Steady"] — a list of two text strings.
# A *for* loop repeats a block once for each item in a list,
# like "for company in companies:".

companies = ["Swift", "Steady"]

# We store the time each company needs as a *dictionary* —
# a set of name-value pairs, so we can look up "Swift" and get 4.
# A dictionary is like a table you can read by name:
# {"Swift": 4, "Steady": 12}

weeks_needed = {"Swift": 4, "Steady": 12}

# When the simulation starts, no weeks have passed.
weeks_passed = 0

# We also want to show the result at the end, so we use
# a *while* loop — it repeats as long as a condition is true.
# Here, we keep looping while there is any company still working.

while weeks_needed["Swift"] > 0 or weeks_needed["Steady"] > 0:
    # Each pass of this loop is one week.
    weeks_passed = weeks_passed + 1

    # Subtract one week from each company that is still working.
    # 'if' checks a condition; '> 0' means 'greater than zero'.
    if weeks_needed["Swift"] > 0:
        weeks_needed["Swift"] = weeks_needed["Swift"] - 1
    if weeks_needed["Steady"] > 0:
        weeks_needed["Steady"] = weeks_needed["Steady"] - 1

# Now both are done. Print the results.
# 'print()' shows text on the screen.
print("Swift finished in", 4, "weeks")
print("Steady finished in", 12, "weeks")
print("Swift was", 12 / 4, "times faster")

# Run this mentally:
# weeks_passed = 0 → 1 → 2 → ... → 4, then Swift hits 0,
# but Steady still has 8 left. Loop continues until Steady hits 0 at week 12.
# The division 12 / 4 = 3.0 shows the speed ratio.
```


## What the code does

The code uses three Python building blocks you may never have seen — a list, a dictionary, a while loop — and uses them the way you would in any real script. The *list* `companies` holds the names of the two companies, just to have them in a container you can iterate over. The *dictionary* `weeks_needed` maps each company name to the number of weeks it needs, so you can write `weeks_needed["Swift"]` and get 4. The *while* loop repeats the block until a condition is false — here, until both companies have 0 weeks left. Inside the loop we increment `weeks_passed` and decrement each company's weeks if they are still positive. When the loop ends, the `print` statements write the results. Notice that we never explicitly write 'weeks_passed' in the print for each company — we hard-coded the 4 and 12, which is a simplification; a fuller model would store these in a variable. But the point is to show the *mechanics* of a while loop doing arithmetic. There's no NumPy or PyTorch version because this is a discrete event simulation, not a matrix operation; you could use NumPy for the arithmetic, but it would be overkill, and the plain Python is clearer for teaching the concept.


## Common misconceptions

**Dynamic capabilities = just having a flexible team** — If we hire adaptable people and tell them to be flexible, we automatically have dynamic capabilities.

Individual flexibility is a raw material, not a capability. A capability is an *organised* pattern — a repeatable way of changing that works regardless of who happens to be on duty. If your reconfiguration depends on one brilliant person improvising, you have a 'hero' situation, not a capability. When that person leaves, the ability leaves with them. In fact: Dynamic capabilities live in processes, routines, and systems — in how the organisation notices, decides, and changes. People are essential to run them, but the capability outlasts any individual.

**Dynamic capabilities are only for tech companies** — That's a Silicon Valley term; our industry doesn't change that fast, so we don't need it.

Every industry changes eventually — regulations, demographics, competitors, supply chains, AI. The pace may be slow, but the *need to reconfigure* is universal. A bank that ignores online payments for too long loses its customer base exactly as fast as a film company that ignores streaming. In fact: Dynamic capabilities matter at every speed; only the *urgency* differs. Slow industries have a longer runway, but a missed change still ends the advantage.

**Dynamic capabilities are the same as agility** — "Agility" and "dynamic capabilities" mean the same thing — being fast.

Speed is only one part. A company can be fast at *executing the wrong change* — that is not a dynamic capability, it is wasted motion. Dynamic capabilities imply *sensing* the right change, *seizing* it, and only then moving. Agility without sensing is like a race car with no steering. In fact: Dynamic capabilities = sensing + seizing + transforming. Speed matters, but so does direction.

**Dynamic capabilities are just about reorganising the org chart** — Reconfiguring means changing who reports to whom. Move the boxes on the chart and we've done it.

Reconfiguring is about *resources* — people, skills, technology, routines, cash — and about the *behaviours* that use them. A re-org without a change in how work actually happens is a new diagram, not a new capability. In fact: Transformation changes what the organisation can *do*, not just what its chart looks like.

**Dynamic capabilities are a one-time project** — Once we modernise our processes, we'll have dynamic capabilities forever.

Competitors keep changing, technology keeps advancing, customer expectations keep rising. A capability that was dynamic in 2015 may be static by 2025. Dynamic means *continually* dynamic, not 'once dynamic'. In fact: Dynamic capabilities are a permanent capacity for renewal, not a shortcut to a finished state.


## In short

Dynamic capabilities are an organisation's ability to sense change, seize the opportunity it creates, and transform itself around it. Advantage does not come from having the best resources; it comes from being able to reconfigure those resources faster than rivals. The three capacities — sensing, seizing, transforming — are the unit of analysis. Talent management is one of the levers that builds this capability, because reconfiguration is done through people, their skills, and the routines they follow. Remember the two delivery companies: Swift finished in 4 weeks, Steady in 12; the ratio, not the starting position, decided who led the market. That ratio — speed of reconfiguration — is the whole mathematics behind the concept.


## Where this is used

Dynamic capabilities are not a software library you import; they are a way of thinking about how an organisation competes. But that thinking directly shapes how AI systems are built and used. Consider the large language model (LLM) itself. An LLM is a *fixed* resource — a huge neural network trained once. Its ability to *sense* is the ability to read text and infer what the user wants. Its ability to *seize* is to generate a response. But an LLM, unaided, cannot *transform* — it cannot update its own weights after training. That is why real-world LLM systems are built as *pipelines*: an LLM hooked up to a retrieval system that fetches fresh documents, a tool-calling mechanism that can query a database, a memory store that persists across conversations. Each of these is a *dynamic capability* built on top of the static LLM, and the organisation that puts these pieces together fastest has the advantage — not the organisation that merely has the best LLM. In small language models running on phones, the constraint is different: you don't have the compute to train a model from scratch, so you use a smaller model fine-tuned on your specific domain. The *dynamic* part comes from updating the model frequently as your product changes — a weekly fine-tuning pipeline, an automated data pipeline that feeds new examples into the training process. A developer building an agentic system — one that plans, calls tools, and acts in loops — is literally building *sensing* (parsing user input and environment state), *seizing* (deciding which tool to call next), and *transforming* (updating its internal state or its tool list). Each step of the loop is a reconfiguration. In retrieval-augmented generation, the dynamic capability is in the *index*: you continually re-embed your document store as new documents arrive, re-rank results as interaction data comes in, move from dense to hybrid retrieval as search quality degrades. A developer works with libraries like LangChain, LlamaIndex, or Haystack to chain these steps, and the *design* is the dynamic capability: the ability to swap out a retriever, change a prompt, add a tool, without rewriting the whole system. In everyday applied machine learning — recommendation, vision, forecasting, fraud — the dynamic capability shows up as *model retraining*: a fraud model trained on last month's transactions becomes stale as fraudsters adapt, so the team rebuilds it every week on fresh labeled data. The developer writes a training script, a data-validation check, a deployment hook; the *speed* of that retraining loop is the organisation's dynamic capability. What breaks when this is done badly is exactly what the cricket analogy predicted: the model's accuracy falls, the system becomes brittle, and a competitor who retrains faster takes the market. So the concrete takeaway: whenever you hear 'AI systems', think 'static model + dynamic pipes'. The dynamic pipes are the capabilities; the model is the resource.


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Read this on the web: https://basavapurushottam.com/learn-ai/dynamic-capabilities
